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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystr�m sampling with active selection of support vectors. The methods are illustrated with several examples.
- Sales Rank: #3544805 in Books
- Brand: Brand: World Scientific Publishing Company
- Published on: 2002-11-14
- Original language: English
- Number of items: 1
- Dimensions: 9.48" h x .84" w x 6.52" l, 1.23 pounds
- Binding: Hardcover
- 308 pages
- Used Book in Good Condition
Most helpful customer reviews
0 of 0 people found the following review helpful.
Almost what I needed ...
By Peter F.
I am using LS-SVMlab software toolbox and this book is helpful to understand the background. This book is NOT the tutorial I am searching for. Unfortunately, neither is the User's Guide. I'm looking for a detailed tutorial on NARX time series implementation. Still, this book is helpful.
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